Automated techniques can be used to determine solutions based on an underlying data set, such as a set of measurements. As a specific example, machine learning techniques can be applied to such a data set to determine a “best fit” equation. More generally, machine learning solutions may be represented as mathematical or logical constructs that approximate theoretically ideal solutions. Once a solution has been derived using such automated techniques, it is often relatively straightforward to apply the solution to an existing problem. For example, a best fit equation may be used to estimate future measurements simply by evaluating the equation for a given set of parameters.
Automated solutions may also reflect some information about the nature of the underlying problem to which they are applied. However, this information may not be readily apparent from simply observing such an automated solution. As an example, a machine learning solution represented as an equation may include coefficients that are learned by the machine learning algorithm. While the coefficients may be effective for accurately estimating future outcomes based on the set of parameters, it may not be apparent to a human observer how these coefficients reflect the relative contribution of the various parameters to the outcomes.